variance

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- | In [[probability]] theory and statistics, variance is the expectation of the squared deviation of a [[random variable]] from its mean. Informally, it measures how far a set of (random) numbers are spread out from their average value. Variance has a central role in statistics, where some ideas that use it include descriptive statistics, statistical inference, hypothesis testing, goodness of fit, and Monte Carlo sampling. Variance is an important tool in the sciences, where statistical analysis of data is common. The variance is the square of the standard deviation, the second central moment of a distribution, and the covariance of the random variable with itself. | + | In [[probability]] theory and statistics, variance is the expectation of the [[squared deviation]] of a [[random variable]] from its mean. Informally, it measures how far a set of (random) numbers are spread out from their average value. Variance has a central role in statistics, where some ideas that use it include descriptive statistics, statistical inference, hypothesis testing, goodness of fit, and Monte Carlo sampling. Variance is an important tool in the sciences, where statistical analysis of data is common. The variance is the square of the standard deviation, the second central moment of a distribution, and the covariance of the random variable with itself. |

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[[Variance]] between [[provider]]s in the neurosurgical field leads to inefficiencies and poor patient [[outcome]]s. Evidence based [[guideline]]s (EBGs) have been developed as a means of pooling the body of [[evidence]] in the [[literature]] to provide [[clinician]]s with the most comprehensive [[data]]-driven [[recommendation]]s. However, these EBGs are not being implemented well into the clinician [[workflow]], and therefore clinicians are left to make [[decision]]s with incomplete [[information]]. Equally underutilized are [[electronic health record]]s (EHRs), which house enormous health [[data]], but which have failed to capitalize on the power of that '[[big data]].' Early attempts at EBGs were rigid and not adaptive, but with the current advances in data [[informatics]] and [[machine learning algorithm]]s, it is now possible to integrate 'big data' and rapid data processing into clinical decision support tools. As we strive towards variance reduction in healthcare, the integration of 'big data' and EBGs for decision-making are key. | [[Variance]] between [[provider]]s in the neurosurgical field leads to inefficiencies and poor patient [[outcome]]s. Evidence based [[guideline]]s (EBGs) have been developed as a means of pooling the body of [[evidence]] in the [[literature]] to provide [[clinician]]s with the most comprehensive [[data]]-driven [[recommendation]]s. However, these EBGs are not being implemented well into the clinician [[workflow]], and therefore clinicians are left to make [[decision]]s with incomplete [[information]]. Equally underutilized are [[electronic health record]]s (EHRs), which house enormous health [[data]], but which have failed to capitalize on the power of that '[[big data]].' Early attempts at EBGs were rigid and not adaptive, but with the current advances in data [[informatics]] and [[machine learning algorithm]]s, it is now possible to integrate 'big data' and rapid data processing into clinical decision support tools. As we strive towards variance reduction in healthcare, the integration of 'big data' and EBGs for decision-making are key. |

variance.txt ยท Last modified: 2019/02/27 20:26 by administrador